README.md

---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- peft
- qlora
- qualitative-research
- open-coding
---

# qwen25_7b QLoRA Open-Coding Adapter

This PEFT adapter fine-tunes `Qwen/Qwen2.5-7B-Instruct` to produce exactly one concise open code
for an input utterance or qualitative text segment.

## Output schema

Task mode: `single_code`.

```json
{"code": "short analytical label"}
```

## Held-out verification

- Rows: 100
- Valid JSON rate: 1.000
- Non-empty rate: 1.000
- Exact set match: 0.160
- Mean set F1: 0.160
- Average generated codes: 1.000
- Verification passed: True

Exact match is reported as a format and regression diagnostic, not as a complete
measure of open-code quality. Valid abstractive labels may differ in wording.

## Loading

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "Likich/open-coding-qwen25_7b-single_code-qlora")
```

The repository contains adapter weights, tokenizer metadata, training metadata,
held-out verification metrics, and sample predictions. It does not contain the
full base-model weights.